Three development stages of mobile geographic information system technology
- Vol. 26, Issue 12, Pages: 2399-2410(2022)
Received：21 October 2020，
Published：07 December 2022
DOI： 10.11834/jrs.20210428
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摘要
为了明晰的理清移动地理信息系统技术发展脉络，本文简述了信息技术变革是如何推动该技术向嵌入式时代、移动互联网时代、智能物联网时代不断演变，并依次分析了每个时代的技术架构及其典型应用，在此基础上提出了集成全球导航定位、5G、人工智能和计算机视觉等信息技术于一体的新一代移动地理信息系统概念，分析了其具备的泛在化、实时化、智能化特征，论述了支撑3大特征的跨平台内核、同步定位与全息高精度导航地图、语义地图和智能决策3种核心技术，总结其发展和应用的变化趋势，对后续移动GIS的发展有一定的指导意义。
Abstract
The development of Mobile Geographic Information System (MGIS)
which is driven by the change of information technology
is clarified and divided into three eras in this work: embedded
mobile Internet
and intelligent Internet Of Things (IOT) era.
At the end of 1990s
with the completion of Global Positioning System (GPS) deployment in the United States
the functions related to information acquisition of desktop GIS system were transplanted to Personal Digital Assistant (PDA) and other embedded devices to facilitate field data acquisition. MGIS entered the “embedded era” combined with GPS. In this period
the stand-alone version of MGIS has been successfully applied in the field data collection of land
forestry
surveying
mapping
and other industries. Although MGIS had some online functions at that time
the bandwidth of mobile network was not enough to support high-frequency network GIS services.
MGIS has gradually entered the “mobile Internet era” with the rise of 3G/4G and other broadband mobile networks and the popularity of intelligent mobile terminals (especially Android mobile phones). In this period
the core module of MGIS was translated from Global Navigation Satellite System (GNSS) to wireless communication network. The most typical applications in this era are the map APP developed by Google
Baidu
and other electronic map service providers and its related location-based services APP. At this time
MGIS has extended to the entire geographic information industry chains
involving data collection
data processing
platform software
industry applications
etc. “Cloud+End” constitutes a new ecosystem of geographic information. However
due to the problems of cloud computing
such as less real-time
insufficient bandwidth
and large energy consumption
which are not conducive to data security and privacy
MGIS in this era is still in the traditional artificial ground operation stage
making real-time spatial analysis
target recognition
and other intelligent processing difficult.
The MGIS technology gradually enters the “intelligent IOT era” around 2019 with the ubiquitous development of IOT
especially the development of Computer Vision (CV)
Artificial Intelligence (AI)
5G mobile communication
edge computing
and other technologies. The main technical features of this stage are intelligent
real-time
and ubiquitous GIS
and the system architecture evolves into “cloud+edge+end”. In this era
everyone is a sensor and plotter. A large number of intelligent sensors
such as cameras and radars integrated on ground mobile platforms (wearable devices
vehicles
etc.) and air mobile platforms (unmanned aerial vehicles
etc.) have emerged
which can help us locate and provide holographic map information
such as acoustic-optic-magnetic information. These IOT terminals can be used as carriers of MGIS. The massive raw data of the collected images
videos
locations
and other data are no longer uploaded to the cloud
but are analyzed and processed in real time by AI and other technologies on the network edge devices. Only the results are transmitted to the cloud. This mode greatly reduces the pressure of network bandwidth
data center power consumption
and system delay and enhances the service response ability. Furthermore
the risk of network data leakage is greatly reduced
and user data security and privacy are protected because users no longer upload privacy or sensitive data (only stored on network edge devices).
On this basis
a new generation of MGIS is proposed
which integrates GNSS
5G
AI
CV
and other information technologies. The ubiquitous
real-time
and intelligent features of this technology are analyzed
and three core technologies
including cross-platform kernel
simultaneous localization and mapping
pan-information-based high-precision navigation map
semantic map
and intelligent decision-making
are discussed. The development trend and direction are also predicted.
关键词
Keywords
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Han LIU 清华大学 地球系统科学系 教育部地球系统模拟重点实验室
Peng GONG 清华大学 地球系统科学系 教育部地球系统模拟重点实验室;清华大学 中国城市研究院;东亚迁徙鸟类与栖息地生态学野外观测研究站
LI Xiaofeng 中国科学院海洋研究所 海洋环流与波动重点实验室;中国科学院海洋研究所 海洋观测与预报重点实验室和海洋环流与波浪重点实验室
CHEN Wantai 中国科学院海洋研究所 海洋环流与波动重点实验室;中国科学院海洋研究所 海洋观测与预报重点实验室和海洋环流与波浪重点实验室;中国科学院大学 海洋学院
ZHOU Yinfei 中国科学院海洋研究所 海洋环流与波动重点实验室;中国科学院海洋研究所 海洋观测与预报重点实验室和海洋环流与波浪重点实验室;中国科学院大学 海洋学院
GUO Yuan 中国科学院海洋研究所 海洋环流与波动重点实验室;中国科学院海洋研究所 海洋观测与预报重点实验室和海洋环流与波浪重点实验室;中国科学院大学 海洋学院
LIU Fangyi 中国科学院海洋研究所 海洋环流与波动重点实验室;中国科学院海洋研究所 海洋观测与预报重点实验室和海洋环流与波浪重点实验室;中国科学院大学 海洋学院
WANG Jiaqi 中国科学院东北地理与农业生态研究所;中国科学院大学
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Tsinghua Urban Institute, Tsinghua University
East Asian Migratory Birds and Habitat Ecology Field Observation and Research Station
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University
School of Oceanography, University of Chinese Academy of Sciences
Key Laboratory of Ocean Observation and Forecasting and Key Laboratory of Ocean Circulation and Waves, Institute of Oceanography, Chinese Academy of Sciences
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